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As brackish turbid waters exit San Francisco Bay, one of the largest estuaries in the U.S. West Coast, they form the San Francisco Bay Plume (SFBP), which spreads offshore and influences the Gulf of the Farallones (GoF), an ecologically significant region in the California Current System that is also home to three National Marine Sanctuaries. This paper provides the first observationally based investigation of the spatio-temporal variability of the SFBP, using a plume tracking algorithm applied to more than two decades (2002-2023) of ocean color data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard satellites Aqua and Terra. The turbid SFBP spreads radially, extending 10-20 km offshore around 50% of the time, and during extreme discharge events (<1% of the time), the plume can reach nearly 60 km offshore to the shelf break. The greatest variability in frequency of plume occurrence was observed 10-20 km offshore and it was largely explained by the seasonal cycle (80% of total variance), linked primarily to seasonal changes in river discharge. Largest plume areas (determined by summing up all pixel areas weighted by their respective fraction of plume occurrence) were observed during winter and smallest during summer, occupying on average 24% and 1.5% of GoF area, respectively. Beyond 20-30 km offshore, variability in frequency of plume occurrence was dominated by the intraseasonal band (50-80% of total variance), attributed to plume response to synoptic wind-forcing and/or filaments and eddies, while the interannual band played a secondary role in the plume variability (<20% of total variance). Finally, a multivariable linear regression model of the turbid SFBP area was created to explore the potential predictability of the plume’s influence in the GoF. The model included the annual and semi-annual cycles and discharge anomalies (deseasoned and detrended), and despite its simplicity, it explained over 78% of total variance of the turbid SFBP area. Therefore, it could be a useful tool for scientists and stakeholders to better understand how management actions on freshwater supply can have consequences offshore beyond the Golden Gate and help guide future management decisions in this ecologically important region. 

Abstract Microbial gene loss is hypothesized to be beneficial when gene function is costly, and the gene product can be replaced via cross-feeding from a neighbor. However, cross-fed metabolites are often only available at low concentrations, limiting the growth rates of gene-loss mutants that are dependent on those metabolites. Here we define conditions that support a loss of function mutant in a three-member bacterial community of (i) N2-utilizing Rhodopseudomonas palustris as an NH4+-excreting producer, (ii) N2-utilizing Vibrio natriegens as the ancestor, and (iii) a V. natriegens N2-utilizaton mutant that is dependent on the producer for NH4+. Using experimental and simulated cocultures, we found that the ancestor outcompeted the mutant due to low NH4+ availability under uniform conditions where both V. natriegens strains had equal access to nutrients. However, spatial structuring that increasingly segregated the mutant from the ancestor, while maintaining access to NH4+ from the producer, allowed the mutant to avoid extinction. Counter to predictions, mutant enrichment under spatially structured conditions did not require a growth rate advantage from gene loss and the mutant coexisted with its ancestor. Thus, cross-feeding can originate from loss-of-function mutations that are otherwise detrimental, provided that the mutant can segregate from a competitive ancestor. 

In recent work, Backendal, Haller, and Paterson identified several exploitable vulnerabilities in the cloud storage provider MEGA. They demonstrated an RSA key recovery attack in which a malicious server could recover a client’s private RSA key after 512 client login attempts. We show how to exploit additional information revealed by MEGA’s protocol vulnerabilities to give an attack that requires only six client logins to recover the secret key. Our optimized attack combines several cryptanalytic techniques. In particular, we formulate and give a solution to a variant of the hidden number problem with small unknown multipliers, which may be of independent interest. We show that our lattice construction for this problem can be used to give improved results for the implicit factorization problem of May and Ritzenhofen. 

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling